Modeling Single-Crystal Battery Materials: From Fundamental Understanding to Performance Evaluation.

Area of Science:

• Materials Science
• Electrochemistry
• Computational Modeling

Background:

• Rechargeable battery performance hinges on material physicochemical properties and microstructure.
• Single-crystal (SC) morphologies show promise for overcoming polycrystalline (PC) limitations in electrodes and electrolytes.
• SC materials offer tunable charge transport and enhanced cycling stability.

Purpose of the Study:

• To review computational modeling approaches for investigating SC battery materials.
• To elucidate structure-property-performance relationships in SC cathodes, anodes, and solid-state electrolytes.
• To identify modeling limitations and propose solutions for rational SC battery component design.

Main Methods:

• Atomistic, mesoscale, and continuum-level computational modeling.
• Machine learning methodologies for predictive analysis.
• Investigation of crystallographic anisotropy, size effects, and facet-dependent properties.

Main Results:

• Computational models reveal critical factors governing SC battery material electrochemical behavior.
• Predictive modeling elucidates processing-structure-property-performance links for SC components.
• Unique SC characteristics like anisotropy and facet dependence are explored.

Conclusions:

• Integrating simulations with experiments accelerates the design of SC battery components.
• Computational modeling is key to optimizing SC materials for advanced energy storage.
• Addressing modeling limitations will further advance SC battery technology.